Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation

Abstract

Because of the incorporation of discontinuous fibers, steel fiber-reinforced concrete (SFRC) outperforms regular concrete. However, due to its complexity and limited available data, the development of SFRC strength prediction techniques is still in its infancy when compared to that of standard concrete. In this paper, the compressive strength of steel fiber-reinforced concrete was predicted from different variables using the Random forest model. Case studies of 133 samples were used for this aim. To design and validate the models, we generated training and testing datasets. The proposed models were developed using ten important material parameters for steel fiber-reinforced concrete characterization. To minimize training and testing split bias, the approach used in this study was validated using the 10-fold Cross-Validation procedure. To determine the optimal hyperparameters for the Random Forest algorithm, the Grid Search Cross-Validation approach was utilized. The root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) between measured and estimated values were used to validate and compare the models. The prediction performance with RMSE=5.66, R2=0.88 and MAE=3.80 for the Random forest model. Compared with the traditional linear regression model, the outcomes showed that the Random forest model is able to produce enhanced predictive results of the compressive strength of steel fiber-reinforced concrete. The findings show that hyperparameter tuning with grid search and cross-validation is an efficient way to find the optimal parameters for the RF method. Also, RF produces good results and gives an alternate way for anticipating the compressive strength of SFRC

Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete

“…As the simplest ML technique, MLR was implemented to predict the CS of SFRC and showed R 2 of 0.888, RMSE of 6.301, and MAE of 5.317. Al-Abdaly et al 50 reported that MLR algorithm (with R 2 = 0.64, RMSE = 8.68, MAE = 5.66) performed poorly in predicting the CS behavior of SFRC. Khademi et al 51 used MLR to predict the CS of NC and found that it cannot be considered an accurate model (with R 2 = 0.518).…”

Advancement of management information system for discovering fraud in master card based intelligent supervised machine learning and deep learning during SARS-CoV2

“…We built the best hyper parameter search by sckit learn's grid search function with cross-validation. Because every ML model had its own set of hyper-parameters, the overall search for each model was unique ( Al-Abdaly et al, 2021 ). When selecting hyper-parameters, it is important to keep in mind that performance is a primary factor in determining which parameters to use.…”

Analysis of Fire Risk Factors in Seoul, Korea, by Machine Learning

“…Therefore, a hyperparameter optimization process is required. (21,22) There are no established standards or methods for selecting hyperparameters in machine learning. It is common to find the hyperparameter that minimizes the error by changing it according to the actual data of machine learning.…”

“…It is common to find the hyperparameter that minimizes the error by changing it according to the actual data of machine learning. (21) The optimal hyperparameter can be derived through various trial and error tests. Therefore, in our work, a k-fold cross validation method was used.…”